Apache Spark Python - Processing Column Data - Overview of Predefined Functions in Spark

Data Engineering Spark
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This article provides a detailed guide on working with Spark SQL functions for beginners. It covers key concepts, step-by-step instructions, hands-on tasks, and a summary to help readers understand and practice using various functions in Spark SQL.

Pre-defined Functions

The article includes practical examples of using functions to process column data in Spark SQL DataFrames. It covers important functions for string manipulation, date manipulation, and more. Readers are encouraged to try out the provided code snippets using Spark SQL to enhance their skills.

Projection

Projection involves selecting, adding, or dropping columns in a DataFrame. Functions like select, withColumn, and drop are commonly used for projection.

Filtering

Filtering data involves selecting rows based on specific conditions. Functions like filter or where help to filter data in a DataFrame.

Grouping data

Grouping data involves aggregating data based on a specific key. Functions like groupBy are used to group data in a DataFrame.

Sorting data

Sorting data involves arranging records in a specific order. Functions like sort or orderBy are used to sort data in a DataFrame.

Watch the video tutorial embedded below to get practical insights into working with Spark SQL functions.
Watch the video tutorial here

Hands-On Tasks

  1. Perform a projection using the select function to select specific columns from a DataFrame.
  2. Filter data in a DataFrame based on a specific condition using the filter function.

Conclusion

In this article, we covered essential concepts related to Spark SQL functions, including projection, filtering, grouping, and sorting. By practicing the hands-on tasks and exploring further with the community, readers can deepen their understanding of working with Spark SQL functions.